Biometric authentication based on PCG and ECG signals: present status and future directions

Abo‐Zahhad, Mohammed; Ahmed, Sabah M.; Abbas, Sherif N.

doi:10.1007/s11760-013-0593-4

Cited by 78 publications

(42 citation statements)

References 35 publications

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“…The electrocardiogram (ECG), resulting from the electrical conduction through the heart needed for its contraction, is one of the most recent traits to be explored for biometric purposes [4,5]. Despite being far from as developed or widespread as face or fingerprint biometrics, the ECG offers unique advantages in terms of universality, uniqueness, permanence, and liveness assurance, that attest its potential for the recognition of individuals [5,6].…”

Section: Introductionmentioning

confidence: 99%

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

Pinto

Cardoso

Lourenço

et al. 2017

Sensors

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Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering, which was able to render ensemble heartbeats of significantly higher quality. Discrete Cosine Transform (DCT) and Haar transform features were extracted and fed to decision methods based on Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models - Universal Background Models (GMM-UBM) classifiers, for both identification and authentication tasks. Additional techniques of user-tuned authentication and past score weighting were also studied. The method’s performance was comparable to some of the best recent state-of-the-art methods (94.9% identification rate (IDR) and 2.66% authentication equal error rate (EER)), despite lesser results with scarce train data (70.9% IDR and 11.8% EER). It was concluded that the method was suitable for biometric recognition with driving electrocardiogram signals, and could, with future developments, be used on a continuous system in seamless and highly noisy settings.

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Section: Introductionmentioning

confidence: 99%

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

Pinto

Cardoso

Lourenço

et al. 2017

Sensors

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“…Examples of these bio-electrical signals include Electro-Encephalo-Gram (EEG), Phono-Cardio-Gram (PCG), Electro-Cardio-Gram (ECG), and Electro-Oculo-Gram (EOG). One of the most recent nonmedical application for these bio-electrical signals is biometric authentication [1], [2], [3]. Commercial biometric authentication systems currently include finger-print, voice, and face.…”

Section: Introductionmentioning

confidence: 99%

A Novel Biometric Approach for Human Identification and Verification Using Eye Blinking Signal

Abo‐Zahhad

Ahmed

Abbas

2015

IEEE Signal Process. Lett.

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In this paper, a novel technique is adopted for human recognition based on eye blinking waveform extracted from electro-oculogram signals. For this purpose, a database of 25 subjects is collected using Neurosky Mindwave headset. Then, the eye blinking signal is extracted and applied for identification and verification tasks. The pre-processing stage includes empirical mode decomposition to isolate electro-oculogram signal from brainwaves. Then, time delineation of the eye blinking waveform is utilized for feature extraction. Finally, linear discriminant analysis is adopted for classification. Based on the achieved results, the proposed system can identify subjects with best accuracy of 97.3% and verify them with an equal error rate of 3.7%. The obtained results in this paper confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for human recognition task.

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“…As shown in [19], there are different frequency domain feature extraction. They can be Short Time Fourier Transform [19], Wavelet Transform ( [11], [20], [21], [22]), chaos extractor ( [23]), Pulse Active Width ( [24]) and others.…”

Section: State Of the Artmentioning

confidence: 99%

Intruder recognition using ECG signal

Pasero

Balzanelli

Caffarelli

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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The electrocardiogram (ECG) is becoming a promising technology for biometric human identification. Usually ECG is used for health measurements and this is useful for biometric applications to state that the subject under analysis is live. But an individual identification shouldn't require a classical ECG clinical analysis where several contacts are applied to the person to be identified. In literature, ECG biometric recognition is usually studied for the recognition of a subject within a group of known subjects. In this paper, a new approach is considered. The aim of our embedded wearable controller is to authorize a subject or to reject him, labeling as an intruder unknown to the system. The study used 40 healthy subjects: two authorized and 38 intruders. A one-lead ECG trace has been recorded from the wrists of subjects, features have been extracted using a combination of Autocorrelation and Discrete Cosine Transform (AC/DCT) and then classified using a Multilayer Perceptron. Results show that intruder recognition can be performed with a success rate equal to 100%.

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Biometric authentication based on PCG and ECG signals: present status and future directions

Cited by 78 publications

References 35 publications

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

A Novel Biometric Approach for Human Identification and Verification Using Eye Blinking Signal

Intruder recognition using ECG signal

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